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Intracranial Pressure

Overview of attention for book
Cover of 'Intracranial Pressure & Neuromonitoring XVI'

Table of Contents

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    Book Overview
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    Chapter 1 Cerebral Perfusion Pressure Variability Between Patients and Between Centres
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    Chapter 2 Pre-hospital Predictors of Impaired ICP Trends in Continuous Monitoring of Paediatric Traumatic Brain Injury Patients
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    Chapter 3 Prognosis of Severe Traumatic Brain Injury Outcomes in Children
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    Chapter 4 Do ICP-Derived Parameters Differ in Vegetative State from Other Outcome Groups After Traumatic Brain Injury?
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    Chapter 5 Cerebral Arterial Compliance in Traumatic Brain Injury
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    Chapter 6 The Cerebrovascular Resistance in Combined Traumatic Brain Injury with Intracranial Hematomas
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    Chapter 7 Computed Tomography Indicators of Deranged Intracranial Physiology in Paediatric Traumatic Brain Injury
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    Chapter 8 Mean Square Deviation of ICP in Prognosis of Severe TBI Outcomes in Children
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    Chapter 9 KidsBrainIT: A New Multi-centre, Multi-disciplinary, Multi-national Paediatric Brain Monitoring Collaboration
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    Chapter 10 Increased ICP and Its Cerebral Haemodynamic Sequelae
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    Chapter 11 What Determines Outcome in Patients That Suffer Raised Intracranial Pressure After Traumatic Brain Injury?
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    Chapter 12 Visualisation of the ‘Optimal Cerebral Perfusion’ Landscape in Severe Traumatic Brain Injury Patients
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    Chapter 13 Is There a Relationship Between Optimal Cerebral Perfusion Pressure-Guided Management and PaO2/FiO2 Ratio After Severe Traumatic Brain Injury?
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    Chapter 14 Cognitive Outcomes of Patients with Traumatic Bifrontal Contusions
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    Chapter 15 Non-invasive Intracranial Pressure Assessment in Brain Injured Patients Using Ultrasound-Based Methods
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    Chapter 16 Analysis of a Minimally Invasive Intracranial Pressure Signals During Infusion at the Subarachnoid Spinal Space of Pigs
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    Chapter 17 Comparison of Different Calibration Methods in a Non-invasive ICP Assessment Model
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    Chapter 18 An Embedded Device for Real-Time Noninvasive Intracranial Pressure Estimation
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    Chapter 19 Transcranial Bioimpedance Measurement as a Non-invasive Estimate of Intracranial Pressure
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    Chapter 20 Pulsed Electromagnetic Field (PEMF) Mitigates High Intracranial Pressure (ICP) Induced Microvascular Shunting (MVS) in Rats
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    Chapter 21 Volumetric Ophthalmic Ultrasound for Inflight Monitoring of Visual Impairment and Intracranial Pressure
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    Chapter 22 Does the Variability of Evoked Tympanic Membrane Displacement Data (V m) Increase as the Magnitude of the Pulse Amplitude Increases?
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    Chapter 23 Analysis of a Non-invasive Intracranial Pressure Monitoring Method in Patients with Traumatic Brain Injury
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    Chapter 24 A Wearable Transcranial Doppler Ultrasound Phased Array System
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    Chapter 25 Quantification of Macrocirculation and Microcirculation in Brain Using Ultrasound Perfusion Imaging
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    Chapter 26 HDF5-Based Data Format for Archiving Complex Neuro-monitoring Data in Traumatic Brain Injury Patients
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    Chapter 27 Are Slow Waves of Intracranial Pressure Suppressed by General Anaesthesia?
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    Chapter 28 Critical Closing Pressure During a Controlled Increase in Intracranial Pressure
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    Chapter 29 Effect of Mild Hypocapnia on Critical Closing Pressure and Other Mechanoelastic Parameters of the Cerebrospinal System
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    Chapter 30 Occurrence of CPPopt Values in Uncorrelated ICP and ABP Time Series
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    Chapter 31 Simultaneous Transients of Intracranial Pressure and Heart Rate in Traumatic Brain Injury: Methods of Analysis
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    Chapter 32 Increasing the Contrast-to-Noise Ratio of MRI Signals for Regional Assessment of Dynamic Cerebral Autoregulation
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    Chapter 33 Comparing Models of Spontaneous Variations, Maneuvers and Indexes to Assess Dynamic Cerebral Autoregulation
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    Chapter 34 ICP and Antihypertensive Drugs
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    Chapter 35 ICP: From Correlation to Causation
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    Chapter 36 A Waveform Archiving System for the GE Solar 8000i Bedside Monitor
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    Chapter 37 Deriving the PRx and CPPopt from 0.2-Hz Data: Establishing Generalizability to Bedmaster Users
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    Chapter 38 Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data
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    Chapter 39 Multi-Scale Peak and Trough Detection Optimised for Periodic and Quasi-Periodic Neuroscience Data
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    Chapter 40 Room Air Readings of Brain Tissue Oxygenation Probes
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    Chapter 41 What Do We Mean by Cerebral Perfusion Pressure?
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    Chapter 42 Investigation of the Relationship Between the Burden of Raised ICP and the Length of Stay in a Neuro-Intensive Care Unit
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    Chapter 43 Pressure Reactivity-Based Optimal Cerebral Perfusion Pressure in a Traumatic Brain Injury Cohort
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    Chapter 44 Spaceflight-Induced Visual Impairment and Globe Deformations in Astronauts Are Linked to Orbital Cerebrospinal Fluid Volume Increase
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    Chapter 45 Ventriculomegaly in the Elderly: Who Needs a Shunt? A MRI Study on 90 Patients
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    Chapter 46 Is There a Link Between ICP-Derived Infusion Test Parameters and Outcome After Shunting in Normal Pressure Hydrocephalus?
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    Chapter 47 Mathematical Modelling of CSF Pulsatile Flow in Aqueduct Cerebri
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    Chapter 48 Cerebrospinal Fluid and Cerebral Blood Flows in Idiopathic Intracranial Hypertension
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    Chapter 49 Significant Association of Slow Vasogenic ICP Waves with Normal Pressure Hydrocephalus Diagnosis
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    Chapter 50 ICP Monitoring and Phase-Contrast MRI to Investigate Intracranial Compliance
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    Chapter 51 Numerical Cerebrospinal System Modeling in Fluid-Structure Interaction
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    Chapter 52 Differential Systolic and Diastolic Regulation of the Cerebral Pressure-Flow Relationship During Squat-Stand Manoeuvres
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    Chapter 53 Normative Ranges of Transcranial Doppler Metrics
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    Chapter 54 Autoregulating Cerebral Tissue Selfishly Exploits Collateral Flow Routes Through the Circle of Willis
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    Chapter 55 ICP Monitoring by Open Extraventricular Drainage: Common Practice but Not Suitable for Advanced Neuromonitoring and Prone to False Negativity
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    Chapter 56 Comparison of Intracranial Pressure and Pressure Reactivity Index Obtained Through Pressure Measurements in the Ventricle and in the Parenchyma During and Outside Cerebrospinal Fluid Drainage Episodes in a Manipulation-Free Patient Setting
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    Chapter 57 Visualizing Cerebrovascular Autoregulation Insults and Their Association with Outcome in Adult and Paediatric Traumatic Brain Injury
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    Chapter 58 Assessing Cerebral Hemodynamic Stability After Brain Injury
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    Chapter 59 Systolic and Diastolic Regulation of the Cerebral Pressure-Flow Relationship Differentially Affected by Acute Sport-Related Concussion
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    Chapter 60 Induced Dynamic Intracranial Pressure and Cerebrovascular Reactivity Assessment of Cerebrovascular Autoregulation After Traumatic Brain Injury with High Intracranial Pressure in Rats
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    Chapter 61 Prediction of the Time to Syncope Occurrence in Patients Diagnosed with Vasovagal Syncope
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    Chapter 62 Statistical Signal Properties of the Pressure-Reactivity Index (PRx)
Attention for Chapter 38: Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data
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Chapter title
Medical Waveform Format Encoding Rules Representation of Neurointensive Care Waveform Data
Chapter number 38
Book title
Intracranial Pressure & Neuromonitoring XVI
Published in
Acta neurochirurgica Supplement, January 2018
DOI 10.1007/978-3-319-65798-1_38
Pubmed ID
Book ISBNs
978-3-31-965797-4, 978-3-31-965798-1
Authors

Ian Piper, Martin Shaw, Christopher Hawthorne, John Kinsella, Laura Moss

Abstract

Technology in neurointensive care units can collect and store vast amounts of complex patient data. The CHART-ADAPT project is aimed at developing technology that will allow for the collection, analysis and use of these big data at the patient's bedside in neurointensive care units. A requirement of this project is to automatically extract and transfer high-frequency waveform data (e.g. ICP) from monitoring equipment to high performance computing infrastructure for analysis. Currently, no agreed data standard exists in neurointensive care for the description of this type of data. In this pilot study, we investigated the use of Medical Waveform Format Encoding Rules (MFER- www.mfer.org-ISO 11073-92001) as a possible data standard for neurointensive care waveform data. Several waveform formats were explored (e.g. XML, DICOM waveform) and evaluated for suitability given existing computing infrastructure constraints, e.g. NHS network capacity and the processing capabilities of existing integration software. Key requirements of the format included a compact data size and the use of a recognised standard. The MFER waveform format (ISO/TS 11073-92001) met both requirements. To evaluate the practicality of the MFER waveform format, seven waveform signals (ICP, ECG, ART, CVP, EtCO2, Pleth, Resp) collected over a period of 8 h from a patient at the Institute of Neurological Sciences in Glasgow were converted into MFER waveform format. The MFER waveform format has two main components: sampling information and frame information. Sampling information describes the frequency of the data sampling and the resolution of the data. Frame information describes the data itself; it consists of three elements: data block (the actual data), channel (each type of waveform data occupies a channel) and sequence (the repetition of the data). All seven waveform signals were automatically and successfully converted into the MFER waveform format. One MFER file was created for each minute of data (total of 479 files, 181 KB each). The MFER waveform format has potential as a lightweight standard for representing high-frequency neurointensive care waveform data. Further work will include a comparison with other waveform data formats and a live trial of using the MFER waveform format to stream patient data over a longer period.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 18%
Unspecified 1 9%
Other 1 9%
Student > Bachelor 1 9%
Student > Doctoral Student 1 9%
Other 2 18%
Unknown 3 27%
Readers by discipline Count As %
Engineering 2 18%
Computer Science 1 9%
Unspecified 1 9%
Medicine and Dentistry 1 9%
Social Sciences 1 9%
Other 0 0%
Unknown 5 45%